class Interface:
"""
User interface for generating counterfactual states and actions.
For each trajectory:
The user can use the `a` and `d` keys to go backward and forward in time to a specific state they wish to
generate a counterfactual explanation from. Once a specific state is found, the user presses `w` to launch the
counterfactual mode.
In the counterfactual mode, the user can control the agent using the keyboard. Once satisfied with their
progress, the user can give up control to the bot to finish the episode by pressing `w`.
The keys for controlling the agent are summarized here:
escape: quit the program
view mode:
d: next time step
a: previous time step
w: select time step and go to counterfactual mode
counterfactual mode:
left: turn counter-clockwise
right: turn clockwise
up: go forward
space: toggle
pageup or x: pickup
pagedown or z: drop
enter or q: done (should not use)
a: undo action
w: roll out to the end of the episode and move to next episode
"""
def __init__(self,
original_dataset: TrajectoryDataset,
counterfactual_dataset: TrajectoryDataset,
policy_factory=None):
self.policy_factory = policy_factory
self.dataset = original_dataset
self.counterfactual_dataset = counterfactual_dataset
self.trajectory_generator = self.dataset.trajectory_generator()
self.navigator: TrajectoryNavigator = None
self.window = None
self.is_counterfactual = False
self.run()
def run(self):
for i, trajectory in enumerate(self.trajectory_generator):
self.saved = False
self.is_counterfactual = False
self.navigator = TrajectoryNavigator(trajectory)
self.window = Window(f'Trajectory {i}')
self.window.reg_key_handler(self.key_handler)
self.reset()
self.window.show(block=True)
if not self.saved:
raise Exception('Continued without saving the trajectory!')
def redraw(self):
step: TrajectoryStep = self.navigator.step()
# if not self.agent_view:
env = step.state
img = env.render('rgb_array', tile_size=32)
# else:
# img = step.observation['image']
# TODO later: figure out when to use the observation instead.
self.window.show_img(img)
def step(self, action=None):
if action is None:
self.navigator.forward()
else:
assert isinstance(self.navigator, CounterfactualNavigator)
self.navigator.forward(action)
self.redraw()
def backward(self):
self.navigator.backward()
self.redraw()
def reset(self):
env = self.navigator.step().state
if hasattr(env, 'mission'):
print('Mission: %s' % env.mission)
self.window.set_caption(env.mission)
self.redraw()
def select(self):
new_navigator = CounterfactualNavigator(
self.navigator.episode,
self.navigator.index,
self.navigator.step(),
policy_factory=self.policy_factory)
self.navigator = new_navigator
self.is_counterfactual = True
print(
f'Starting counterfactual trajectory from {self.navigator.index}')
self.redraw()
def save_trajectory(self):
assert isinstance(self.navigator, CounterfactualNavigator)
self.navigator.store(self.counterfactual_dataset)
self.saved = True
def key_handler(self, event):
print('pressed', event.key)
if event.key == 'escape':
self.window.close()
exit()
return
# if event.key == 'backspace':
# self.reset()
# return
if self.is_counterfactual:
if event.key == 'left':
self.step('left')
return
if event.key == 'right':
self.step('right')
return
if event.key == 'up':
self.step('forward')
return
# Spacebar
if event.key == ' ':
self.step('toggle')
return
if event.key == 'pageup' or event.key == 'x':
self.step('pickup')
return
if event.key == 'pagedown' or event.key == 'z':
self.step('drop')
return
if event.key == 'enter' or event.key == 'q':
self.step('done')
return
if event.key == 'w':
if self.policy_factory is not None:
self.navigator.rollout()
self.save_trajectory()
self.window.close()
if event.key == 'a':
self.backward()
return
if not self.is_counterfactual:
if event.key == 'd':
self.step()
return
if event.key == 'a':
self.backward()
return
if event.key == 'w':
self.select()
return
accuracy = torch.cat((accuracy, correct), 0)
update += 1
# Print logs
if update % args.log_interval == 0:
if args.visualize:
# Visualize last frame of last sample
from gym_minigrid.window import Window
window = Window('gym_minigrid - ' + args.env)
images = images.transpose(1,2)
images = images.transpose(2,3)
print(images[-1].shape)
print(label)
window.show_img(images[-1])
input()
window.close()
duration = int(time.time() - start_time)
header = ["Update", "Time", "Loss", "Accuracy"]
acc = torch.mean(accuracy)
data = [update, duration, sum(losses) / len(losses), acc]
losses = []
over = (acc >= 0.9999)
accuracy = torch.tensor([]).to(device)
txt_logger.info(
"U {} | T {} | L {:.3f} | A {:.4f}"
.format(*data))
if status["update"] == 0: